Techniques, suitable for parallel implementation, for robust 2D model-based object recognition in the presence of sensor error are studied. Models and scene data are represented as local geometric features and robust hypothesis of feature matchings and transformations is considered. Bounds on the error in the image feature geometry are assumed constraining possible matchings and transformations. Transformation sampling is introduced as a simple, robust, polynomial-time, and highly parallel method of searching the space of transformations to hypothesize feature matchings. Key to the approach is that error in image feature measurement is explicitly accounted for. A Connection Machine implementation and experiments on real images are presented.

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Discontinuous dynamical systems and multiagent systems are encountered in numerous engineering applications. This dissertation develops stability and dissipativity of nonlinear dynamical systems with discontinuous right-hand sides, optimality of discontinuous feed-back controllers for Filippov dynamical systems, almost consensus protocols for multiagent systems with innaccurate sensor measurements, and adaptive estimation algorithms using multiagent network identifiers. In particular, we present stability results for discontinuous dynamical systems using nonsmooth Lyapunov theory. Then, we develop a constructive feedback control law for discontinuous dynamical systems based on the existence of a nonsmooth control Lyapunov function de fined in the sense of generalized Clarke gradients and set-valued Lie derivatives. Furthermore, we develop dissipativity notions and extended Kalman-Yakubovich-Popov conditions and apply these results to develop feedback interconnection stability results for discontinuous systems. In addition, we derive guaranteed gain, sector, and disk margins for nonlinear optimal and inverse optimal discontinuous feedback regulators that minimize a nonlinear-nonquadratic performance functional for Filippov dynamical systems. Then, we provide connections between dissipativity and optimality of nonlinear discontinuous controllers for Filippov dynamical systems. Furthermore, we address the consensus problem for a group of agent robots with uncertain interagent measurement data, and show that the agents reach an almost consensus state and converge to a set centered at the centroid of agents initial locations. Finally, we develop an adaptive estimation framework predicated on multiagent network identifiers with undirected and directed graph topologies that identifies the system state and plant parameters online.

The aim of the doctoral thesis is the problem of the primary calibration of acoustic emission sensor. In the introductory chapters, there are the results from the study of literature in field of AE and primary calibration of AE sensor summarized. The practical part of the thesis is aimed on realization of the primary calibration of AE sensors by different methods. The standardized methods of step function calibration and reciprocity calibration were practically realized as a part of the research on the same aparature. The method of reciprocity calibration with wideband excitation was practically realized too. The comparison between results from each used method was realized and the influences of the different sources of uncertainty and the different configuration of the calibration aparature on the step function calibration and the reciprocity calibration were determined. The uncertainties were determined for each realized methods. The recommendations for the calibration by the different methods on the available calibration aparature were presented. All the proceses of the reciprocity primary calibration was automated by the programmed software and the channel switcher. The UT 1000 (s.n. 169) by Physical Acoustic Corporation was used as the calibrated sensor for the comparison of the results of the calibrations.

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